Veridical data science extracts reliable and reproducible information from data, with an enriched technical language to communicate and evaluate empirical evidence in the context of human decisions and domain knowledge. Building and expanding on principles of statistics, machine learning, and the sciences, Yu and Kumbier (PNAS, 2020) propose the predictability, computability, and stability (PCS) framework for veridical data science. The PCS framework is comprised of both a workflow and documentation and aims to provide responsible, reliable, reproducible, and transparent results across the entire data science life cycle for making data driven decisions and knowledge generation. In other words, PCS is a step forward towards quality control and standardization of the data science process or life cycle.
Yu and Kumbier (2020) at https://www.stat.berkeley.edu/~binyu/ps/papers2020/VDS20-YuKumbier.pdf
QnAs with Bin Yu at https://www.stat.berkeley.edu/~binyu/ps/papers2020/VDS20-QnAsBinYu.pdf
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Bin Yu
Statistics and EECS, University of California at Berkeley
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